Most financial service firms, which includes banking and insurance companies, are engaged in a big data project to increase the pace of innovation and uncover game-changing business outcomes. The pressing challenge now is how to drive more continuous value and unearth opportunities more rapidly.

No matter where you might be in your big data journey, the following three-step approach to integrating big data into an analytics strategy can lead to success:

Step One: Outline business objectives and outcomes

To drive continuous and transformational improvements through big data-driven analytics projects, business units IT, marketing, risk, compliance or finance, for example should agree on and outline a mutually beneficial business objective. For instance, driving a better customer experience or improving customer value management. While developing the common objective, financial services firms should also determine the aligned and desired outcomes, such as decreasing fraud and offering more personalized services to customers in real-time.

Once business outcomes have been determined and prioritized, the firm can then decide on the best big data technologies needed to modernize their enterprise infrastructure to better mobilize the data across the business for consumption, enabling it to realize the desired outcomes. It's also important for the business units to decide on how the analytics activity will be tracked to make sure the project is on a path to success.

Step Two: Understand the environment and drive innovation

As desired outcomes are established, banks and insurance companies should seek opportunities to accelerate how they leverage their data to drive optimal value. For example, cloud enabled analytic service environments powered by big data technologies can shorten the previously lengthy technology and business planning cycles.

These environments can not only be used to discover hidden insights rapidly, but they can also be used to, for instance, help a company more deeply understand how these transformative technologies can best work in their enterprise and model how analytic services can be managed and operated across the enterprise. When pursuing this approach, a firm can determine, in an agile and innovative way, how their data and non-traditional technologies can come together to transform the enterprise into a fierce data-driven competitor.

Traditionally, financial services companies only used transactional data such as customer payment and deposit data, but today they can analyze the transactional data along with interaction data such as online, call center, and even social media data. When looking to analyze and uncover insights from the new data types and sources, firms may discover they have a gap in their technology infrastructure that would allow them to manage the new data to reach the specified business objective. As a solution, companies should look to build a hybrid technology environment this can be created by adding emerging technologies such as Hadoop to an established technology infrastructure. As a result, data can be quickly mobilized and analyzed in a cost-effective and timely fashion to chase the business outcome.

The data exploration shouldn't end there. Simultaneously, financial services firms should create and execute on an innovation agenda. Along with seeking the specific outcome, companies can test and play with their data through data discovery techniques to find patterns in the data that weren't clearly evident and could drive value for the business.

For example, banks and insurance companies have identified fraudulent behavior by applying this data discovery technique. One company discovered that people who input information faster into fields online were more fraudulent, and conversely people who spelled the first and last name online with an upper case first letter were less likely to be fraudulent.

Step Three: Mobilize the data

To realize the true value hidden in data, financial services companies should look at this asset as if it were a supply chain, enabling it to flow easily and usefully through the entire organization--and eventually throughout each company's ecosystem of partners. To build a data supply chain, firms should begin by following two important steps: utilize a data service platform that makes all data accessible to those who need it when they need it, and integrate data from multiple sources.

With the new external data sources becoming available that can unearth new insight opportunities and the new big data tools and technology entering the space, a foundation has been established for companies to create an integrated, end-to-end data supply chain for their business and uncover new data insights that can bring a competitive advantage.

Experience the big data benefits

Following are a few examples showcasing how financial services companies are innovating and solving business problems with big data:

Through big data analytics, banks and insurance companies can provide customers with a richer experience. To accomplish this goal, firms should explore micro-segmentation of customer data obtained from all transaction touch points mobile, online, call centers, etc. and analyze it so customers with similar needs can be presented with relevant and timely offers on their desired channel, from a mobile app to social media.

Another example: A bank could create an overlay of customer sentiment data on top of customer survey data. This data analysis could help firms learn if they are giving customers the right service, refunding money for the right reasons, or charging people the right fees. If data insights uncovered negative or incorrect results for the consumer, the bank could then take steps to right the wrongs and improve the customer's experience. In real-time.

Also, a life insurance company could underwrite risk better. For example, a company could text mine decades of hand-written claims adjusters' notes, in its current unstructured data form, and place all the newly created datasets (i.e. attributes of the policy or claim) into a structured database along with the existing online insurance policy documents. The database housing the combined data could provide insurance companies with a better place to start when looking to analyze data to underwrite risk more effectively.

Big data and analytics can provide financial services firms with insights needed to achieve their current goals, from growing customer loyalty to improving business operations, or uncover a new opportunity it didn't know existed. With the growth of big data not slowing down, firms should continue to adopt it as a core capability that is central to their data and analytics strategies to drive better outcomes and a competitive edge.

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